Abstract
We propose a new approach for medical diagnosis by employing intuitionistic fuzzy sets [cf. Atanassov [1, 2]]. Solution is obtained by looking for the smallest distance [cf. Szmidt and Kacprzyk [7, 8]]_ between symptoms that are characteristic for a patient and symptoms describing illnesses considered. We point out advantages of this new concept over the method proposed by De, Biswas and Roy [4] where intuitionistic fuzzy sets were also applied, but the max-min-max rule was used instead of taking into account all, unchanged symptom values as proposed in this article.
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© 2001 Springer-Verlag Berlin Heidelberg
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Szmidt, E., Kacprzyk, J. (2001). Intuitionistic Fuzzy Sets in Intelligent Data Analysis for Medical Diagnosis. In: Alexandrov, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds) Computational Science - ICCS 2001. ICCS 2001. Lecture Notes in Computer Science, vol 2074. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45718-6_30
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DOI: https://doi.org/10.1007/3-540-45718-6_30
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